In general, radiation therapy consists of the use of ionizing radiation to treat living tissue, usually tumors. There are many different types of ionizing radiation used in radiation therapy, including high energy x-rays, electron beams, and proton beams. The process of administering the radiation to a patient can be somewhat generalized regardless of the type of radiation used.
Techniques allow the radiologist to treat a patient from multiple angles while varying the shape and dose of the radiation beam, thereby providing greatly enhanced ability to deliver radiation to a target within a treatment volume while avoiding excess irradiation of nearby healthy tissue. However, this variability in delivering radiation has made the task of developing treatment plans more difficult. Both the target within the treatment volume and any nearby organs may have complex three dimensional shapes adding to the difficulty of preparing a treatment plan.
Treatment planning can start with (1) images of the treatment volume (e.g., slices from CT or MRI scans) and, (2) the desired dose of radiation which is to be delivered to a target, such as a tumor, within the treatment volume, and (3) the maximum dose which can be safely absorbed by tissue structures, such as organs, within the treatment volume that are adjacent to or near the tumor or other target volume. A variety of algorithms have been developed to solve the “inverse problem” of devising and optimizing a specific, three-dimensional treatment plan for irradiating the treatment volume from a variety of angles or, in arc therapy, while the system gantry is moving, to deliver a desired radiation dose to the target while minimizing irradiation of nearby tissue, taking into account the capabilities and physical limitations of the radiotherapy system. Generally speaking, the inverse problem involves optimizing the angles, MLC leaf movements and durations of irradiations. Because of the large number of variables involved and complex matrix manipulations that are required, the algorithms for calculating and optimizing treatment plans require substantial computational time even when using modern high speed computers.
However, the desired dose may not always be possible given the constraints of the system and the particular patient (e.g., geometry of the tumor). Thus, the desired dose distribution may need be modified as part of the optimization process, which can cause further difficulty. Therefore, it is desirable to identify a dose distribution that is achievable.
In knowledge based dose prediction, information from previously planned radiation treatments are used to gain knowledge about what is an achievable dose distribution in a new case. One approach to knowledge based dose prediction is to use a set of the previously planned cases to create a prediction model that is then be used (without needing to store all information related to this training set) to predict the dose for a new case. The dose prediction model can help identify a dose distribution that is achievable and reduces the effort to determine a desired dose.
Currently, dose prediction models are created from training sets of treatment plans that are selected by a human operator. It may prove very time consuming for a human operator to shift through thousands of treatment plans in order to categorize them into different training sets for different models. It is also very difficult for a human operator to evaluate whether all selected treatment plans in a particular training set are representative of the set.
Further, when many predictive models exist, it is difficult to manually select the prediction model that would be most valid for a current patient. Additionally, a user can only choose the model for the current plan from the existing pre-configured models, none of which may provide an accurate prediction for the dose distribution.
Therefore, it is desirable to provide new methods to create and select dose prediction models.